Designing Next-Generation Behavioral Architectures for Interactive Agents
- Theoretical Foundations:
- Computational rationality in NPCs
- Formal models of player-adaptive systems
- Taxonomy of emergent gameplay phenomena
- Applied Techniques:
- Neuroevolutionary approaches (NEAT, HyperNEAT)
- Hierarchical reinforcement learning for long-horizon tasks
- Language model integration for dynamic narrative generation
- Challenge: "Modern game AI relies on patched-together behaviors. I build unified algorithmic frameworks for truly adaptive in-game agents."
- Novel Behavioral Algorithms:
- Hybrid symbolic-neural architectures (e.g., Diffusion Models + Utility Theory)
- Proto-Algorithm: "Dynamic Behavior Trees with Online Bayesian Parameter Learning" (WIP)
- Evaluation Paradigms:
- Quantifying player-perceived agency beyond traditional metrics
- Stress-testing under distributional shift (e.g., player improvisation)
- PhD Proposal: "Behavioral Algorithmics: A New Lens for Game Agent Design"
- With Labs: Implementing algorithms in cognitive robotics simulators
- With Indies: De-risking experimental AI in narrative games
- Critics Needed: For algorithmic elegance peer-review